Mission planning systems provide various elements with instructions on how to perform a given function. The goal of each system is to increase the measure of effectiveness of the function. For example, when searching for a target in a large area, a mission planning system may determine how best to search the area for the target. A current system may follow the general process of finding the target, fixing its location and vector, tracking the target, targeting the target with a weapon, engaging the target with the weapon, assessing the damage caused by the weapon to the target, and then repeating the process until the target is adequately neutralized. This process, however, tends to be static, rigid it its deployment, and unable to adapt to changing circumstances.
One example of a traditional search method is that of sequentially tracing slightly offset ovals over a search area until the entire area has been searched, often referred as the Zamboni method. This method is simple to program, requires little computational complexity, and is easily tested. It is also, however, inefficient and sub-optimal when prior information is available.
Situational awareness has been identified as a key concept to be included in mission planning systems. Situational awareness allows missions to adapt to real-time environments and deviate from a pre-programmed plan. Including situational awareness, however, adds complexity and cost to current systems and may further be unworkable in some urban environments. Further, controlling multiple assets in a given area at present requires increases in both available bandwidth and computing resources.